{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An example showing the plot_silhouette method used by a scikit-learn clusterer\n",
    "\n",
    "In this example, we'll perform a silhouette analysis to the clusters found by our K-Means clustering method. First we'll create an instance of K-Means, then fit it to the data. Afterwards we can pass the `cluster_labels`, i.e. the output of the `fit_predict` method, into the `skplt.metrics.plot_silhouette` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "from __future__ import absolute_import\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.datasets import load_iris as load_data\n",
    "\n",
    "# Import scikit-plot\n",
    "import scikitplot as skplt\n",
    "\n",
    "%pylab inline\n",
    "pylab.rcParams['figure.figsize'] = (14, 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data\n",
    "X, y = load_data(return_X_y=True)\n",
    "\n",
    "# Create an instance of the clusterer then fit\n",
    "kmeans = KMeans(n_clusters=4, random_state=1)\n",
    "cluster_labels = kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NkRapFsRYvs5JP8Q+iOU1K91XQNfqtNNJteHaa5OzIy6SVHMsLVI1iRFif1JO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ee0e504cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot!\n",
    "skplt.metrics.plot_silhouette(X, cluster_labels, cmap='nipy_spectral')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
